'Usage profiles' presentation slideshows

Specialized Techniques

Specialized Techniques. Chapter 11. 11.1 Time-Series Analysis. Time-series Problems: Prediction applications with one or more time-dependent attributes. An Example with Linear Regression. The Stock Index Dataset. Linear Regression Equations for the Stock Index Dataset.

550 views • 44 slides

313 views • 23 slides

Project Five Modeling Details

Project Five Modeling Details. Reliability analysis of ABS A sampling of both SPN and SAN Models is provided Assumptions are identified Models are solved Findings are graphed Comparison gives semi-validation. Assumptions Exponential Failure Rates to allow Markov chain analysis

309 views • 19 slides

Discovery of Aggregate Usage Profiles for Web Personalization

Discovery of Aggregate Usage Profiles for Web Personalization. Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, Jim Wiltshire. School of Computer Science, Telecommunications, and Information Systems DePaul University. Web Personalization. The Problem

284 views • 17 slides

Clustering Applications in Web Mining and Web Personalization

Clustering Applications in Web Mining and Web Personalization. Bamshad Mobasher DePaul University. Clustering Application: Web Usage Mining. Discovering Aggregate Usage Profiles

324 views • 17 slides

Discovery of Aggregate Usage Profiles for Web Personalization

Discovery of Aggregate Usage Profiles for Web Personalization. Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, Jim Wiltshire. School of Computer Science, Telecommunications, and Information Systems DePaul University. Web Personalization. The Problem

174 views • 17 slides

Site Update Idaho National Laboratory

Site Update Idaho National Laboratory. April 2011.

171 views • 10 slides

Usage Profiles

182 views • 8 slides

Data

Data. From Sustrans , a charity that promotes sustainable transport in the UK Responsible for planning and delivering the National Cycle Network 107 automatic counters - #bikes per hour, many operating for over 5 years. Usage profiles. Clustering. Try to find common shapes .

153 views • 8 slides